Geotechnical Zoning of Mexico Valley Subsoil

Home , Valley of Mexico

Ingeniería Investigación y Tecnología, volumen XVII (número 3), julio-septiembre 2016: 297-308 ISSN 1405-7743 FI-UNAM (artículo arbitrado)

Zonificación geotécnica del subsuelo del Valle de México

Juárez-Camarena Moisés Instituto de Ingeniería, UNAM E-mail: [email protected] Méndez-Sánchez Edgar Instituto de Ingeniería, UNAM Auvinet-Guichard Gabriel E-mail: [email protected] Instituto de Ingeniería, UNAM E-mail: [email protected]

Información del artículo: recibido: mayo de 2014, aceptado: enero de 2016

Abstract

A new geotechnical zoning map for the subsoil of Mexico Valley is presented. This proposal is based on a Geographic Information System for Geote- Keywords: chnical Borings (GIS-GB), which contains over 10000 soil profiles. In addition to the geotechnical information, available topographic and geological data • subsoil on the studied area were taken into account. Geostatistical techniques were • borings used to assess the spatial distribution of the thickness of the lacustrine clay • Geographic Information deposits within the area down to the so-called deep deposits. As a result, a System contour map was obtained that was used to update the current geotechnical • Mexico Valley zoning map for Mexico Valley. It has been proposed to include this new map • Geostatistics into the Building Code for the Federal District (Mexico City). • correlogram • estimation • variance of estimation • Kriging • mapping • geotechnical zoning Geotechnical Zoning of Mexico Valley Subsoil

Resumen

Se presenta una nueva zonificación geotécnica para el subsuelo del valle de México. Esta propuesta se basa en un Sistema de Información Geográfica para sondeos geo- Descriptores: técnicos (SIG-GB), que almacena más de 10000 perfiles de suelo. Además de la infor- mación geotécnica, se tomaron en cuenta los datos topográficos y geológicos • subsuelo disponibles relativos a la zona estudiada. Se aplicaron técnicas geoestadísticas para • sondeos evaluar la distribución espacial del espesor de los depósitos arcillosos del área hasta • Sistema de Información los llamados depósitos profundos. Como resultado se obtuvo un mapa de contornos Geográfica que se utilizó para actualizar la zonificación geotécnica para el Valle de México. Se • Valle de México ha propuesto que este nuevo mapa de zonificación geotécnica se incorpore en el nue- • Geoestadística vo Reglamento de Construcciones para el Distrito Federal. • correlograma • estimación • varianza de estimación • Kriging • mapeo • zonificación geotécnica

Introduction Location of study area

The numerous geotechnical borings performed in the The study presented in this paper is focused on the area urban area of Mexico City can be used to obtain a better shown in Figure 1. It includes parts of political delega- knowledge of the subsoil and for improving the accura- tions Álvaro Obregón, Azcapotzalco, Benito Juárez, cy of the existing geotechnical zoning map for regula- Cuauhtémoc, Gustavo A. Madero, Iztacalco, Miguel tory purposes of construction (GDFa, 2004; GDFb, Hidalgo and Venustiano Carranza in the Distrito Fede- 2004). ral and municipalities of Naucalpan, Ecatepec, Neza- To take advantage of the available information, hualcoyotl, Tlalnepantla in the Estado de Mexico computational and informatics tools, such as Geogra- (Figure 1). phical Information Systems as well as powerful mathematical tools based on Geostatistics have been used. Basic information Geographic Information Systems are useful to organize geotechnical information for fast and easy reviewing. To characterize the geological formations and soil de- On the other hand, Geostatistics, defined as the applica- posits typical of Mexico Valley subsoil, it was conside- tion of random functions theory to the description of red necessary to collect data of very different nature the spatial distribution of properties of geological mate- and to integrate and present this information in synthe- rials, provides valuable tools for estimating data such tized form. as thickness of a specific stratum, or local value of a given soil property, taking into account the correlation Topography structure of the medium. Additionally, uncertainty associated to these estimations can be quantified. Figure 2 shows a Shaded Relief Model (SRM) illustrating In recent years, several studies dealing with the the topographic configuration of the area. The model subsoil characterization for different areas within was built from the electronic data edited by Instituto the Valley of Mexico have been published. In that Nacional de Estadística Geografía e Informática (INE- studies, Geostatistical methods have been widely GI, 2010). used to assess the spatial distribution of geotechnical Mexico Valley is a former lacustrine area limited by properties (Jiménez, 2007; Valencia, 2007; Hernández, large topographic elevations: Sierra de Las Cruces, Mon- 2013; Eyssautier, 2014; Juárez, 2014). The results of the- te Alto and Monte Bajo to the west reaching an altitude se works have been taken into account in the geote- of up to 3600 m, Sierra de Guadalupe to the north rea- chnical zoning proposed in this paper. ching an elevation of 2960 m, the eastern Sierra Nevada

298 Ingeniería Investigación y Tecnología, volumen XVII (número 3), julio-septiembre 2016: 297-308 ISSN 1405-7743 FI-UNAM Juárez-Camarena Moisés, Auvinet-Guichard Gabriel, Méndez-Sánchez Edgar

Figure 1. Location of study area

Figure 2. Topographic model for the study area

Ingeniería Investigación y Tecnología, volumen XVII (número 3), julio-septiembre 2016: 297-308 ISSN 1405-7743 FI-UNAM 299 Geotechnical Zoning of Mexico Valley Subsoil

On the west side, the Sierra de las Cruces range is constituted by a line of large volcanoes oriented from NW to SE; the final activity of these volcanoes was of explosive type lea- ding to the formation of extensive volcanic fans constituted by pyro- clastic materials (T) associated with that activity. On the east side, the Sierra de San- ta Catarina range is formed by a line of volcanoes oriented in the WE direction; those are very young volcanoes, so that their eruptive products are lavas (Qv) that are interbedded with alluvial (Qal) and lacustrine (Ql) deposits. Finally, in the south, the Sierra Chichinautzin range is an extensive volcanic field of the quaternary formed of many individual bo- dies, whose volcanic products, mainly lavas (Qv), form a huge mass of rock that separates the Mexico basin from the Cuernava- ca valley.

Geographic information system for geotechnical borings

Figure 3. Geology of the study area (Mooser, 1996) To assess the configuration of typical layers of the subsoil it was taken ad- and the Sierra de Chichinautzin to the south reaching vantage of the profiles of geotechnical borings provided an altitude of 3800 to 3900 m. Within the valley, some by public institutions and private contractors. These bo- isolated volcanic domes such as Peñón de los Baños rings profiles have been incorporated into a Geographic (2288 m), Peñón del Marqués (2372 m), Cerro de la Estrella Information System developed by the Geocomputing (2443 m), Cerro de Xico (2348 m), Cerro de Tlapacoya Laboratory of Institute of Engineering, UNAM (Auvinet (2442 m) and those forming Sierra de Santa Catarina et al., 1995). (2482 m) protrude from the lacustrine area. The Geographic Information System for Geotechnical Borings (GIS-GB) for the study area has been built using ArcMap ver. 9.2 (commercial software). Nowadays, the Geology system includes a database with information on more Figure 3 shows a geological map of the area (Mooser et than 10000 borings (type, date, location, depth, water al., 1966). table level, etc.) and a database of images of geotechni- The central portion of the area consists of lacustrine cal profiles, which can be readily consulted (Figure 4). soft clay deposits (Ql), which are surrounded by allu- Incorporating information from the borings in the vial deposits (Qal) that also extend below the lacustrine system requires pre-processing: the information is criti- deposits. cally reviewed and converted from analog to digital North of the area, the Sierra de Guadalupe range is format of either raster (cell information) or vector (digi- formed mainly of andesitic and dacitic strato-volcanoes tized information) type. topped by acids domes that were formed in their final activity.

300 Ingeniería Investigación y Tecnología, volumen XVII (número 3), julio-septiembre 2016: 297-308 ISSN 1405-7743 FI-UNAM Juárez-Camarena Moisés, Auvinet-Guichard Gabriel, Méndez-Sánchez Edgar

Figure 4. Geographic Information System for Geotechnical Borings

Subsoil model Zone III. Lake, composed of potent deposits of highly compressible clay strata separated by sand layers with Vertical model varying content of silt or clay. These sandy layers are firm to hard their thickness varies from a few centime- The typical soil profile sequence in the lacustrine zone ters to several meters. The lacustrine deposits are often of Mexico City subsoil includes a thin superficial Dry covered superficially by alluvial soils, dried materials Crust (DC), a First Clay Layer (FCL) several tens of me- and artificial fill materials, the thickness of this package ters thick, a First Hard Layer (FHL), a Second Clay Layer can exceed 50 m. (SCL) and the so-called Deep Deposits (DD) (Marsal and Mazari, 1959). Theoretical concepts of geostatistics

Horizontal model To define with better precision the boundary between zones II and III, a contour map for the thickness of soft Article 170, Chapter VIII, of Mexico City Building Code clay deposits, also corresponding to the depth to the (GDFa, 2004), establishes that for regulatory purposes, Deep Deposits was constructed, using geostatistical te- Mexico City is divided into three zones with the fo- chniques. The basic concepts used for this type of llowing general characteristics: analysis are presented in this section.

Zone I. Hills, formed by rocks or hard soils that were Random field (Auvinet, 2002) generally deposited outside the lake area, but where sandy deposits in relatively loose state or soft clays can It has been proposed to consider spatial distribution also be found. In this area, cavities in rocks, sand mines of soil properties and geometrical dimensions of soil caves and tunnels as well as uncontrolled landfills are strata as realizations of random fields (Matheron, common. 1965; Auvinet 2002). This hypothesis makes it possible Zone II. Transition, where deep firm deposits are to use the mathematical tools of the random function found at a depth of 20 m or less, and consisting predo- theory for important applications such as estimation minantly of sand and silt layers interbedded with la- of the variables of interest in a specific point or volume custrine clay layers. The thickness of clay layers is where no measurement have been performed. variable between a few tens of centimeters and meters.

Ingeniería Investigación y Tecnología, volumen XVII (número 3), julio-septiembre 2016: 297-308 ISSN 1405-7743 FI-UNAM 3 01 Geotechnical Zoning of Mexico Valley Subsoil

u = unit vector in the considered direction N = total number of data N(hu) = number of data pairs separated by a distance h in direction u within appropriate tolerance in V(x4, y4) tervals

V(x5, y5) The autocovariance function, commonly anisotropic, represents the degree of linear dependence between the values of the property of interest in two different points. It can be written in the form of a coefficient of autocorrelation without dimension whose value is always between –1 and +1

Correlation coefficient Figure 5. 2D random field (p=2) + CV  VX( ) , VX( hu) ρρVX, VX+= h =h VV( ) ( uu) 2 ( ) (4) σ V (X) Consider a geotechnical variable V(X), either of physi- The autocovariance and the correlation coefficient are cal (i.e. water content), mechanical (i.e. shear strength) not intrinsic properties of the two points X and X + hu, or geometrical nature (i.e. depth or thickness of some they also depend on the population, that is to say, on stratum) defined at pointsX of a given domain Rp (p = 1, the domain in which the field is defined. 2, or 3). In each point of the domain, this variable can be considered as random due to the range of possible va- Correlation distance lues that it can take (Figure 5). The set of these random variables constitutes a random field (Vanmarcke, 1983). The correlation distance (also known as Reach, Influence or range) is the distance beyond which variables V (X) y Structural analysis V (X +hu) are no longer correlated.

To describe a stationary random field, the following pa- The correlation distance, δ(u)= 2a(u), is conventionally rameters and functions are generally used (Matheron, estimated from the experimental correlogram, as: 1965; Deutsch, 1992; Auvinet, 2002) h a = c ρ h dh (5) (uu) ∫ V ( ) Expected value 0 where: h is the first value of h for which ρ (hu) is equal 1 N c V = µ ≅ (1) EVX{ ()} Vi( X) ∑ V( X) to zero. N 11= Variance Estimation 1 N =≅−σµ2 22 var{VX ( )} V( X) ∑ V( XiV) ( X) (2) Geostatistics can be used to estimate the value of a pro- N 11= perty of interest at points of the medium where no mea- Autocovariance function surement has been made. It is then possible to interpolate C VX( ) , VX( += hu) between available data and to define virtual borings, V  cross-sections or configurations of a given stratum within  −µµ +− ≅ the soil. The problem can be generalized to the estimation E{ VX( ) VV( X) VX( hu) ( X)} (3) of the average value of a property in any sub-domain of Nh( u) 1 the studied medium, for example, in a given volume or ≅ VX VX+− hu µ () X2 ∑ ( ii) ( ) V along a certain potentially critical surface. Nh( u) i=1 To reach this objective, linear statistical estimators without bias and with a minimum variance can be used Where (Best linear Unbiased Estimation or “BLUE”). This tech- h = distance between two points X and X + hu of the nique, also known as Ordinary kriging (Krige, 1962; domain Matheron, 1965; Vanmarcke, 1983; Deutsch, 1992; Auvi-

302 Ingeniería Investigación y Tecnología, volumen XVII (número 3), julio-septiembre 2016: 297-308 ISSN 1405-7743 FI-UNAM Juárez-Camarena Moisés, Auvinet-Guichard Gabriel, Méndez-Sánchez Edgar

net, 2002) is also widely used in mining engineering. an RP domain, with p = 2 (area of the former lacustrine The estimate is zone as defined from topographical and geological in- P nn formation). The set of measured values within the R =λ ⋅ +−λµ ⋅ (6) domain (Figure 6) is considered as a random sample of V*1( X) ∑∑i VX( i) iV ii=11= this random field. A structural analysis aimed at defi- The value of the (minimized) variance error associated ning the field parameters was performed after remo- to the estimate, also known as “estimation variance” can ving a linear trend to obtain experimental correlograms also be assessed and correlation distances. Theoretical correlograms were fitted to the data set. n σ2 = +−νλ ⋅ − E(XVX) Var ( ) ∑ iiCX( X) (7) i=1 Statistical description

Accepting the ergodic hypothesis for the random field Configuration of deep deposits depth in study, the main statistical parameters of the depth of Definition of the random field the deep deposits were estimated (Table 1). In Figure 7 the variability of the data is described graphically by The geotechnical zoning map is based on the subsoil means of a histogram. models previously described. In the context of the geos- Table 1. Statistical parameters of the tatistical analysis, the depth of Deep Deposits (DD) is Deep Deposits depth considered as a random field V(X), distributed within Parameter Value No. data 544 Mean, m(m) 32.78 Variance, σ2(m2) 319.28 Standard deviation, σ(m) 17.87 Coefficient of variation 0.5451

Structural analysis

Trend analysis

The general trend of the Deep Depo- sits depth was assessed by means of a linear regression analysis, fitting an equation of the form: V(X) =ax+by+c to the data. The coefficients that best describe this trend are: a = –0.00096398, b = 0.00064913 and c = –955.997663. The corresponding linear regression surface (Figure 8) helps to identify the preferential direction of variation of the random field. The figure shows that the values of the Deep Deposits depth decrease from northwest toward the southeast. The presence of this trend is taken into account to calculate the experimental correlograms and in the esti- Figure 6. Study area and location of the Deep Deposits depth data mations.

Ingeniería Investigación y Tecnología, volumen XVII (número 3), julio-septiembre 2016: 297-308 ISSN 1405-7743 FI-UNAM 303 Geotechnical Zoning of Mexico Valley Subsoil

Histograma

140

120

100

60 Frequency 40

0 0 0 -10 10 -20 20 -30 30 -40 40 -50 50 -60 60 -70 70 -80 80 -90 90-100100 -110 Deep Depositis depth (m)

Figure 7. Histogram of the Deep Deposits depth Figure 8. Linear regression surface of the Deep Deposits depth

Estimation of the autocorrelation coefficient Table 2. Autocorrelation distances of Deep Deposits depth function Direction, Az(°) δ(m) 0 4500 After removing the trend from the original data, a resi- 45 5000 dual field was obtained. The autocorrelation coefficient 90 6000 function was calculated along four preferential direc- 135 4500 tions with azimuth Az = 0°, 45°, 90° and 135°, and steps (Dh) of 250m (Figure 8). These functions describing the It can be seen that influence distances are of the order of correlation structure are shown in Figure 9. The correla- several thousand meters. Some anisotropy is detected. tion distances (d) obtained for each preferential direc- Figure 9 shows that the shortest correlation distance of tion are shown in Table 2. 4500m corresponds to directions Az=0° and 135° and the highest correlation distance of 600m to direction Az=90°.

1 1 Experimental correlogram Experimental correlogram 0.8 Exponential correlogram Exponential correlogram ρ ρ 0.8 , , 0.6 0.6 0.4 0.4 0.2 0.2 0 0 10000 20000 30000 40000 0 -0.2 0 10000 20000 30000 40000 Autocorrelation coefficient Autocorrelation coefficient -0.2 -0.4

-0.4 -0.6 Distance, h(m) Distance, h(m) a) Direction Az = 0° a) Direction Az = 45°

1 1 Experimental correlogram Experimental correlogram Exponential correlogram Exponential correlogram ρ ρ 0.8 0.8 ,

0.6 0.6

0.4 oefficient, 0.4

0.2 0.2

0 0 0 10000 20000 30000 40000 0 10000 20000 30000 40000 Autocorrelation coefficient -0.2 Autocorrelation c -0.2

-0.4 -0.4 Distance, h(m) Distance, h(m) a) Direction Az = 90° a) Direction Az = 135° Figure 9. Directional correlograms for Deep Deposits depth

304 Ingeniería Investigación y Tecnología, volumen XVII (número 3), julio-septiembre 2016: 297-308 ISSN 1405-7743 FI-UNAM Juárez-Camarena Moisés, Auvinet-Guichard Gabriel, Méndez-Sánchez Edgar

The experimental correlograms were fitted to an ex- Visualization ponential function to obtain the spatial correlation model. From the results, a contour map was built showing the spatial distribution of the depth of this layer within the ρ = e-(2h/δ) (8) studied area (Figure 10). The results of the estimation can also be represented by a surface map, assigning the value of the depth to the vertical coordinate (elevation) Estimation of each point (Figure 11). Figures 10 and 11 show that the largest Deep Depo- The expected value and estimation standard deviation sits depths are located in the area of the former Chalco of the depth of the Deep Deposits were obtained at all Lake, in the south part of the lacustrine area. In this nodes of a regular grid conveniently defined, using the area, the Deep Deposits depth reaches values as high as technique of Ordinary Kriging (Deutsch and Journel, 90 and 100m. 1992). Conservatively, a correlation distance δ=5000m was Geotechnical zoning map considered in direction Az=45° and δ=4500m in direction Az=135°. The final estimate of the field was obtai- The map of Figure 10 has immediate practical implica- ned reinstating the linear trend into the results. tions, and is useful to update the geotechnical zoning Additionally, the estimation variance was obtained. map of Federal District. The curves of equal depth of Deep Deposits are also useful in earthquake engineering to evaluate the site effects expected at some specific place. Taking into account the definition of the transition zone pre- N 2165000 viously indicated (GDFa, 2004) and using the map of Figure 10, it was " C A R A C O L " T E X C O C O 2160000 possible to define the boundary li- COORDINATE Y COORDINATE nes between zones II and III. After MONTEVIDEO EJE 5 NTE defining this boundary line and ma- 2155000 P E R I F É R I C O CANAL DE SALES king the appropriate settings, the A U T O P . MÉX . - T E X . new geotechnical zoning map, 2150000 shown in Figure 12, was build. This A E R O P U E R T O R E F O R M A map has been proposed for integra-

V I A D U C T O 2145000 tion into the Building Code.

R E Y E S - T E X C O C O The purpose of this map is to

C I R C U I T O contribute to improving the current 2140000 I N T E R I O R I N S U R G E N T E S

Z A R A G O Z A geotechnical zoning map and esta-

P E R I F É R I C O blishing more precise boundaries to 2135000 A V. T L A H U A C help better define the location of di- P E R I F É R I C O

PROL. DIV. DEL NORTE

T L A L P A N fferent soil types in the Valley of

DISTRITO FEDERAL 2130000 TLAHUAC - CHALCOESTADO DE MÉXICO C. X I C O Mexico. However, it is necessary to remark that the map should be up-

XOCHIMILCO - TULYEHUALCO

TLAHUAC - TULYEHUALCO dated in periodic form, integrating 2125000 the new available information. It should also be stressed that 2120000 the geotechnical zoning map only 470000 475000 480000 485000 490000 495000 500000 505000 510000 515000 provides a general orientation and COORDINATE X Zone I in no case should be used to avoid Zones II and III Graphic scale the traditional geotechnical surveys 0 1 2.5 5 10 15 20 Km of geotechnical that must be performed for each project as required by Figure 10. Contour map of Deep Deposits depth the building code.

Ingeniería Investigación y Tecnología, volumen XVII (número 3), julio-septiembre 2016: 297-308 ISSN 1405-7743 FI-UNAM 305 Geotechnical Zoning of Mexico Valley Subsoil

Conclusions

Recent advances in the geotechnical characterization of the subsoil of Mexico Valley using a Geographic In- formation System for Geotechnical Bo- rings (GIS-GB) that includes more than 10,000 soil profiles have been presented. Geostatistical techniques were used to define a model of the compressible lacustrine soils lower boundary also known as Deep Depo- sits. It can be concluded that Geosta- tistical methods provide a rational tool for interpreting the available geotechnical information and to evaluate the spatial variability of the subsoil. They can be useful to eliminate a large part of the subjec- tivity generally introduced in tradi- Figure 11. Representative surface of estimated depth of Deep Deposits tional stratigraphical interpretations and will certainly be used much more frequently in the future. N 2165000 The availability of increasingly accurate information about the dis-

" C A R A C O L " T E X C O C O tribution of materials and index and 2160000 mechanical properties in Mexico COORDINATE Y COORDINATE

MONTEVIDEO EJE 5 NTE Valley subsoil has immediate impli-

2155000 P E R I F É R I C O CANAL DE SALES cations for updating the building

A U T O P . MÉX . - T E X . code and for planning of future works and is deemed to be useful 2150000

A E R O P U E R T O R E F O R M A for civil engineers working in the area. V I A D U C T O 2145000 The geotechnical zoning map R E Y E S - T E X C O C O shown in this paper only provides a C I R C U I T O 2140000 I N T E R I O R general orientation and in no case I N S U R G E N T E S Z A R A G O Z A should it be used to avoid the tradi-

P E R I F É R I C O

A V. T L A H U A C tional geotechnical surveys studies 2135000 P E R I F É R I C O PROL. DIV. DEL NORTE that must be performed for each T L A L P A N project as emphasized in the buil-

DISTRITO FEDERAL 2130000 TLAHUAC - CHALCOESTADO DE MÉXICO C. X I C O ding code.

XOCHIMILCO - TULYEHUALCO

TLAHUAC - TULYEHUALCO 2125000 Acknowledgments

The authors acknowledge the va- 2120000 470000 475000 480000 485000 490000 495000 500000 505000 510000 515000 luable support of Dirección General COORDINATE X de Apoyo al Personal Académico, Zone I UNAM, of Distrito Federal Govern- Zone II Graphic scale 0 1 2.5 5 10 15 20 Km ment and to various private compa- Zone III nies that provided geotechnical Figure 12. Geotechnical zoning map proposed for Mexico City building code information for this work. The con-

306 Ingeniería Investigación y Tecnología, volumen XVII (número 3), julio-septiembre 2016: 297-308 ISSN 1405-7743 FI-UNAM Juárez-Camarena Moisés, Auvinet-Guichard Gabriel, Méndez-Sánchez Edgar

tribution of A. Zuñiga to the geological description pre- Jiménez O. Caracterización geoestadística del subsuelo de la zona po- sented in this paper is also acknowledged. niente del valle de México, (Master degree thesis), SEPI, ESIA- IPN, México, 2007. References Juárez M. Aplicación de la Geoestadística a la Descripción del Subsuelo del Valle de México, (Master degree thesis), SEPI, ESIA-IPN, Auvinet G. Procesos estocásticos, Notes on Stochastic Processes, Mexico, 2001. DEPFI-UNAM, Mexico, 1987. Juárez M. and Auvinet G. Caracterización geoestadística del sub- Auvinet G., Juárez M., Méndez E., Ovando E. Sistema de informa- suelo del Valle de México. Proceedings of 2nd international ción geográfica para sondeos geotécnicos, Proceedings, thX Pan- congress: Métodos numéricos en ingeniería y ciencias aplica- American Conference on Soil Mechanics and Geotechnical das, Vol. 1, Guanajuato, Mexico, 2002, pp.287-296. Engineering, vol. 1, pp. 312-324, Guadalajara, Mexico, 1995. Juárez M. Análisis geoestadístico del subsuelo de la zona lacustre del Auvinet G. Uncertainty in geotechnical engineering, XVIth Nabor Valle de México. Caracterización geoestadística del subsuelo del Va- Carrillo Lecture, Mexican Society for Soil Mechanics, Queré- lle de México, (Doctoral thesis), Doctoral Program in Enginee- taro, Mexico, 2002. ring, UNAM, México, 2014. Deutsch C. and Journel A. Geostatistical Software Library, GSLIB, Krige D.G. Statistical application in mine evaluation, J. Institute Mine New York, Oxford University Press, 1992. Survey, South Africa, 1962. Eyssautier A. Caracterización geotécnica del subsuelo de la zona sur del Marsal R. and Mazari M. El subsuelo de la Ciudad de México, Facul- valle de México con aplicación a una obra de infraestructura, (Pro- tad de Ingeniería, Vol. I y II, UNAM, Mexico, 1959. fessional thesis), FI-UNAM, México, 2014. Matheron G. Les variables généralisées et leur estimation, Masson et Gobierno del Distrito Federal (GDFa). Reglamento de construc- Cie, France, 1965. ciones para el Distrito Federal, Gaceta Oficial del Distrito Fede- Mooser F., Montiel A., Zuñiga. A. Nuevo Mapa Geológico de las ral, 29 de enero, Mexico, 2004. Cuencas de México, Toluca y Puebla, Comisión Federal de Gobierno del Distrito Federal (GDFb). Normas Técnicas Comple- Electricidad, First Edition, Mexico, 1996. mentarias para Diseño y Construcción de Cimentaciones para el Valencia J.D. Contribución a la zonificación geotécnica de la zona norte Distrito Federal, Official Gazette of Distrito Federal, Mexico, 2004. del Valle de México, (Master degree thesis), ESIA-UZ, IPN, Hernández F. Caracterización geotécnica del subsuelo de la zona norte Mexico, 2007. de la cuenca de México, (Master degree thesis), SEPI, ESIA-IPN, Vanmarcke E. Random fields, analysis and synthesis, The Massachu- Mexico, 2013. setts Institute of Technology Press, Cambridge, Massachu- Instituto Nacional de Estadística Geografía e Informática (INEGI). setts, USA, 1983. Topographic data obtained from LiDAR technique for metropolitan zone of Mexico Valley, Mexico, 2010.

Ingeniería Investigación y Tecnología, volumen XVII (número 3), julio-septiembre 2016: 297-308 ISSN 1405-7743 FI-UNAM 307 Geotechnical Zoning of Mexico Valley Subsoil

Citation for this article:

Chicago style citation Juárez-Camarena, Moisés, Gabriel Auvinet-Guichard, Edgar Mén- dez-Sánchez. Geotechnical Zoning of Mexico Valley Subsoil. Inge- niería Investigación y Tecnología, XVII, 03 (2016): 297-308.

ISO 690 citation style Juárez-Camarena M., Auvinet-Guichard G., Méndez-Sánchez E. Geotechnical Zoning of Mexico Valley Subsoil. Ingeniería Investi- gación y Tecnología, volume XVII (issue 3), July-September 2016: 297-308.

Semblanzas de los autores

Moisés Juárez-Camarena. Is a civil engineer by Escuela Superior de Ingeniería y Arqui- tectura (ESIA) of Instituto Politécnico Nacional (IPN), Mexico. He obtained a master in science degree in soil mechanics by ESIA-IPN and a doctor in engineering degree (soils mechanics) within the master and doctorate program in engineering of Universidad Nacional Autónoma de México (UNAM). Currently, he is a re- search engineer at the Geocomputing Laboratory of Instituto de Ingeniería, UNAM. He is a member of the Mexican Society for Geotechnical Engineering and of the International Society for Soil Mechanics and Geotechnical Engineering where he participates in the “Technical Committee on Geotechnical Infrastructure for Mega- cities and New Capitals“. Gabriel Auvinet-Guichard. Got his doctorate in engineering degree from Facultad de In- geniería, Universidad Nacional Autónoma de México (UNAM). He is a researcher at Instituto de Ingeniería, UNAM and professor in the master and doctorate program in engineering of UNAM. He has been president of the Mexican Society for Soil Mechanics and vice-president for North America of the International Society for Soil Mechanics and Geotechnical Engineering. He currently directs the Labora- tory of Geocomputing of Instituto de Ingeniería, UNAM. Edgar Méndez-Sánchez. Is a civil engineer by Escuela Superior de Ingeniería y Arquitec- tura (ESIA) of Instituto Politécnico Nacional (IPN), Mexico. He obtained a master in soil mechanics as part of the master and doctoral program in engineering of Universidad Nacional Autónoma de México (UNAM). He is an academic techni- cian at Instituto de Ingeniería, UNAM. Currently he is member of the Mexican Society for Geotechnical Engineering and of the International Society for Soil Me- chanics and Geotechnical Engineering where he participates in the “Technical Committee on Geotechnical Infrastructure for Megacities and New Capitals“.

308 Ingeniería Investigación y Tecnología, volumen XVII (número 3), julio-septiembre 2016: 297-308 ISSN 1405-7743 FI-UNAM